Getting Started

  collapse = TRUE,
  comment = "#>"



The goal of parsermd is to extract the content of an R Markdown file to allow for programmatic interactions with the document's contents (i.e. code chunks and markdown text). The goal is to capture the fundamental structure of the document and as such we do not attempt to parse every detail of the Rmd. Specifically, the yaml front matter, markdown text, and R code are read as text lines allowing them to be processed using other tools.


You can install the development version of parsermd from GitHub with:


Parsing Rmds

This is a basic example which shows you the basic abstract syntax tree (AST) that results from parsing a simple Rmd file,

rmd = parsermd::parse_rmd(system.file("minimal.Rmd", package = "parsermd"))

The R Markdown document is parsed and stored in a flat, ordered list object containing tagged elements. By default the package will present a hierarchical view of the document where chunks and markdown text are nested within headings, which is shown by the default print method for rmd_ast objects.


If you would prefer to see the underlying flat structure, this can be printed by setting use_headings = FALSE with print.

print(rmd, use_headings = FALSE)

Additionally, to ease the manipulation of the AST the package supports the transformation of the object into a tidy tibble with as_tibble or (both return a tibble).


and it is possible to convert from these data frames back into an rmd_ast.

as_ast( as_tibble(rmd) )

Finally, we can also convert the rmd_ast back into an R Markdown document via as_document

  sep = "\n"

Working with the AST

Once we have parsed an R Markdown document, there are a variety of things that we can do with our new abstract syntax tree (ast). Below we will demonstrate some of the basic functionality within parsermd to manipulate and edit these objects as well as check their properties.

rmd = parse_rmd(system.file("hw01-student.Rmd", package="parsermd"))

Say we were interested in examining the solution a student entered for Exercise 1 - we can get access to this using the rmd_select function and its selection helper functions, specifically the by_section helper.

rmd_select(rmd, by_section( c("Exercise 1", "Solution") ))

To view the content instead of the AST we can use the as_document() function,

rmd_select(rmd, by_section( c("Exercise 1", "Solution") )) %>%

Note that this gives us the Exercise 1 and Solution headings and the contained markdown text, if we only wanted the markdown text then we can refine our selector to only include nodes with the type rmd_markdown via the has_type helper.

rmd_select(rmd, by_section(c("Exercise 1", "Solution")) & has_type("rmd_markdown")) %>%

This approach uses the tidyselect & operator within the selection to find the intersection of the selectors by_section(c("Exercise 1", "Solution")) and has_type("rmd_markdown"). Alternative the same result can be achieved by chaining multiple rmd_selects together,

rmd_select(rmd, by_section(c("Exercise 1", "Solution"))) %>%
  rmd_select(has_type("rmd_markdown")) %>%


One useful feature of the by_section() and has_label() selection helpers is that they support glob style pattern matching. As such we can do the following to extract all of the solutions from our document:

rmd_select(rmd, by_section(c("Exercise *", "Solution")))

Similarly, if we wanted to just extract the chunks that involve plotting we can match for chunk labels with a "plot" prefix,

rmd_select(rmd, has_label("plot*"))

ast as a tibble

As mentioned earlier, the ast can also be represented as a tibble, in which case we construct several columns using the properties of the ast (sections, type, and chunk label).

tbl = as_tibble(rmd)

All of the functions above also work with this tibble representation, and allow for the same manipulations of the underlying ast.

rmd_select(tbl, by_section(c("Exercise *", "Solution")))

As the complete ast is store directly in the ast column, we can also manipulate this tibble using dplyr or similar packages and have these changes persist. For example we can use the rmd_node_length function to return the number of lines in the various nodes of the ast and add a new length column to our tibble.

tbl_lines = tbl %>%
  dplyr::mutate(lines = rmd_node_length(ast))


Now we can apply a rmd_select to this updated tibble

rmd_select(tbl_lines, by_section(c("Exercise 2", "Solution")))

and see that our new lines column is maintained.

Note that using the rmd_select function is optional here and we can also accomplish the same task using dplyr::filter or any similar approach

tbl_lines %>%
  dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution")

As such, it is possible to mix and match between parsermd's built-in functions and any of your other preferred data manipulation packages.

One small note of caution is that when converting back to an ast, as_ast, or document, as_document, only the structure of the ast column matters so changes made to the section columns, type column, or the label column will not affect the output in any way. This is particularly important when headings are filtered out, as their columns may still appear in the tibble while they are no longer in the ast - rmd_select attempts to avoid this by recalculating these specific columns as part of the subsetting process.

tbl %>%
  dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution", type == "rmd_chunk")

tbl %>%
  dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution", type == "rmd_chunk") %>%
  as_document() %>% 

tbl %>%
  rmd_select(by_section(c("Exercise 2", "Solution")) & has_type("rmd_chunk")) %>%
  as_document() %>% 

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parsermd documentation built on May 20, 2021, 5:08 p.m.